Deep learning for survival prediction in triple-negative breast cancer: development and validation in real-world cohorts
Abstract Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous disease, highlighting the need for better patient stratification to guide treatment. We developed a deep learning-based survival model and an individualized prognosis system using data from 37,818 TNBC patients in the S...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-16331-8 |
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| author | Yiyue Xu Butuo Li Bing Zou Bingjie Fan Shijiang Wang Jinming Yu Taotao Dong Linlin Wang |
| author_facet | Yiyue Xu Butuo Li Bing Zou Bingjie Fan Shijiang Wang Jinming Yu Taotao Dong Linlin Wang |
| author_sort | Yiyue Xu |
| collection | DOAJ |
| description | Abstract Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous disease, highlighting the need for better patient stratification to guide treatment. We developed a deep learning-based survival model and an individualized prognosis system using data from 37,818 TNBC patients in the SEER database (split into training [65%], validation [17.5%], and test [17.5%] sets). The survival model, built using the pysurvival algorithm, achieved strong performance (C-index: 0.824 in validation set, 0.816 in test set), outperforming traditional methods (CPH: 0.781 and 0.785; RSH: 0.779 and 0.766). External validation on a real-world cohort confirmed its robustness (C-index: 0.758). Our individualized prognosis system also showed higher predictive accuracy than traditional AJCC-TNM staging (AUC 0.821 vs. 0.771). These tools improve TNBC prognosis assessment, enable better patient stratification, and provide clinicians with significant treatment recommendations. |
| format | Article |
| id | doaj-art-3212d009c90c44059e8b00f0495e2bc2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3212d009c90c44059e8b00f0495e2bc22025-08-24T11:17:21ZengNature PortfolioScientific Reports2045-23222025-08-0115111310.1038/s41598-025-16331-8Deep learning for survival prediction in triple-negative breast cancer: development and validation in real-world cohortsYiyue Xu0Butuo Li1Bing Zou2Bingjie Fan3Shijiang Wang4Jinming Yu5Taotao Dong6Linlin Wang7Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesDepartment of Obstetrics and Gynecology, Qilu Hospital of Shandong UniversityDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical SciencesAbstract Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous disease, highlighting the need for better patient stratification to guide treatment. We developed a deep learning-based survival model and an individualized prognosis system using data from 37,818 TNBC patients in the SEER database (split into training [65%], validation [17.5%], and test [17.5%] sets). The survival model, built using the pysurvival algorithm, achieved strong performance (C-index: 0.824 in validation set, 0.816 in test set), outperforming traditional methods (CPH: 0.781 and 0.785; RSH: 0.779 and 0.766). External validation on a real-world cohort confirmed its robustness (C-index: 0.758). Our individualized prognosis system also showed higher predictive accuracy than traditional AJCC-TNM staging (AUC 0.821 vs. 0.771). These tools improve TNBC prognosis assessment, enable better patient stratification, and provide clinicians with significant treatment recommendations.https://doi.org/10.1038/s41598-025-16331-8Triple-negative breast cancerDeep learningPrognosis modelSurvival prediction system |
| spellingShingle | Yiyue Xu Butuo Li Bing Zou Bingjie Fan Shijiang Wang Jinming Yu Taotao Dong Linlin Wang Deep learning for survival prediction in triple-negative breast cancer: development and validation in real-world cohorts Scientific Reports Triple-negative breast cancer Deep learning Prognosis model Survival prediction system |
| title | Deep learning for survival prediction in triple-negative breast cancer: development and validation in real-world cohorts |
| title_full | Deep learning for survival prediction in triple-negative breast cancer: development and validation in real-world cohorts |
| title_fullStr | Deep learning for survival prediction in triple-negative breast cancer: development and validation in real-world cohorts |
| title_full_unstemmed | Deep learning for survival prediction in triple-negative breast cancer: development and validation in real-world cohorts |
| title_short | Deep learning for survival prediction in triple-negative breast cancer: development and validation in real-world cohorts |
| title_sort | deep learning for survival prediction in triple negative breast cancer development and validation in real world cohorts |
| topic | Triple-negative breast cancer Deep learning Prognosis model Survival prediction system |
| url | https://doi.org/10.1038/s41598-025-16331-8 |
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